To understand more about what translators are, it’s important to first understand what they aren’t. Translators are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.

Their idea is that focusing on developing and hiring professionals with these talents will alleviate the challenges of developing and hiring the forever rare data scientists. Perhaps this is because I’m in the Bay Area, where LinkedIn’s Workforce Report lists Perl/Python/Ruby as the most abundant skills, but it seems to me that domain expertise, within an industry and a company, is significantly more rare than a “working knowledge of AI and analytics” (which the authors recommend acquiring by taking their courses).

Create analytic throughput

If the answer isn’t hiring more data scientists, and it’s not training a new layer to put in-between business leaders and data scientists, then what is it? It’s increasing the analytic throughput of your existing analytic organizations.

Analytic organizations spend 60% of their time getting to insight, but just 27% of that time is spent on actual analysis, the rest is on finding and preparing data.

Instead of building larger and more complex analytic bureaucracies, enable your existing organizations to increase their throughput.

Half of time capacity of analytics organizations is spent on repeating work that’s already been done somewhere else, or unsuccessful efforts. (Data Pros Waste Half of Their Time Chasing Costly Data). Instead of building larger and more complex analytic bureaucracies, enable your existing organizations to increase their throughput. You can achieve this by reviewing the bottlenecks in your current analytic organizations for ease of data access, concurrency, support of a variety of modern and traditional languages and tools.

Stop trying to throw more bureaucracy at the inability to sit down, ask a question and get an answer. Your analytic organization can deliver more insights faster if you enable it to achieve this goal.

Matt Reubendale is a Business Analytics Leader living in San Jose, CA. He’s spent his career helping business leaders leverage analytics to have significant impacts on their organizations. He’s helped organizations realize the value of statistical, geospatial, machine learning, and emerging analytic practices across retail, supply chain, healthcare and manufacturing.